Operations Research and Financial Engineering

Study mode:On campus Study type:Full-time Languages: English
Local:$ 48.9 k / Year(s) Foreign:$ 48.9 k / Year(s) Deadline: Dec 31, 2024
9 place StudyQA ranking:4701 Duration:2 years

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The ORFE department is geared towards educating students whose ultimate goal is to get a Ph.D. The admission rate for the M.S.E. degree is very low. Applicants interested in an M.S.E. degree from ORFE are urged to identify and contact a faculty member in whose area of research they would like to work. Admission will be based on not only qualifications of applicant, but also requires support of at least one faculty member who expresses an interest to supervise the applicant. Students enrolled in this program are eligible for financial support in the form of research or teaching assistantships if such funds are available. Applicants who are primarily interested in a Master's degree in Finance should apply for the Master in Finance(link is external) at the Bendheim Center for Finance. The School of Engineering provides more information regarding the Master of Science in Engineering program(link is external).

The M.S.E. program has a strong research focus reflected in the requirement of a thesis.   The M.S.E. degree is usually completed within two academic years of full-time study.

Courses:

The course requirements are fulfilled by successfully completing ten one-semester courses, two of which are required research courses (ORF 509 and 510).

Thesis:

The M.S.E. program has a strong research focus reflected in the requirement of a thesis. Upon completion and acceptance of the thesis by the department

  • FIN 501 Asset Pricing I: Pricing Models and Derivatives 
  • ORF 504 Financial Econometrics 
  • ORF 505 Statistical Analysis of Financial Data 
  • ORF 509 Directed Research IUnder the direction of a faculty member, Ph.D. and M.S.E. students carry out research, write a report each, and present the results. Of these, 509 is normally taken during the first year of study. Doctoral students should complete 510 one semester prior to taking the general examination.
  • ORF 510 Directed Research IIUnder the direction of a faculty member, Ph.D. and M.S.E. students carry out research, write a report each, and present the results. Of these, 509 is normally taken during the first year of study. Doctoral students should complete 510 one semester prior to taking the general examination.
  • ORF 511 Extramural Summer ProjectSummer research project designed in conjunction with the student's advisor and an industrial, NGO, or government sponsor, that will provide practical experience relevant to the student's course of study. Start date no earlier than June 1. A research report and sponsor's evaluation are required.
  • ORF 515 Asset Pricing II: Stochastic Calculus and Advanced Derivatives (also 
  • ORF 522 Linear and Nonlinear OptimizationTheoretical concepts underlying linear programming, with computer implementations of some of the different methods. The topics covered include duality theory, the simplex method, interior point methods, related numerical issues, and modeling paradigms.
  • ORF 523 Convex and Conic OptimizationAn introduction to the central concepts needed for studying the theory, algorithms, and applications of nonlinear optimization problems. Topics covered include first- and second-order optimality conditions; unconstrained methods, including steepest descent, conjugate gradient, and quasi-Newtonian methods; constrained active-set methods; and duality theory and Lagrangian methods. Prerequisite: linear optimization.
  • ORF 524 Statistical Theory and MethodsA graduate level introduction to statistical theory and methods. It introduces some of the most important and commonly-used principles of statistical inference and covers the statistical theory and methods for point estimation, confidence intervals, and hypothesis testing, and the applications of the fundamental theory to linear models and categorical data.
  • ORF 525 Statistical Learning and Nonparametric EstimationAn introduction to the most important and broadly utilized statistical methods used in many scienti¿c data analysis, including general linear, mixed-e¿ects, generalized linear models, regression and ANOVA models. The methodological power of statistics will be emphasized. Objectives of this course are to give students a solid understanding of these methods, and o¿er them experience in applying these methods to real data using statistical computing packages and interpreting results. For master's/Ph.D. students and advanced undergraduates.
  • ORF 526 Probability TheoryGraduate introduction to probability theory beginning with a review of measure and integration. Topics include random variables, expectation, characteristic functions, law of large numbers, central limit theorem, conditioning, martin- gales, Markov chains, and Poisson processes.
  • ORF 527 Stochastic CalculusAn introduction to stochastic analysis based on Brownian motion. Topics include local martingales, the Ito integral and calculus, stochastic differential equations, the Feynman-Kac formula, representation theorems, Girsanov theory, and applications in finance.
  • ORF 531 Computational Finance in C++ 
  • ORF 534 Quantitative Investment Management 
  • ORF 535 Financial Risk Management
  • ORF 538 PDE Methods for Financial MathematicsAn introduction to analytical and computational methods common to financial engineering problems. Aimed at PhD students and advanced masters students who have studied stochastic calculus, the course focuses on uses of partial differential equations: their appearance in pricing financial derivatives, their connection with Markov processes, their occurrence as Hamilton-Jacobi-Bellman equations in stochastic control problems, and analytical, asymptotic, and numerical techniques for their solution.
  • ORF 542 Stochastic Control and Stochastic Differential GamesDeterministic optimal control, dynamic programming, and Pontryagin maximum principle. Controlled diffusion processes and stochastic dynamic programming. Hamilton-Jacobi-Bellman equation, viscosity solutions. Merton problem, singular optimal control, option pricing via utility maximization.
  • ORF 544 Stochastic OptimizationThis course provides a unified presentation of stochastic optimization, cutting across classical fields including dynamic programming (including Markov decision processes), stochastic programming, (discrete time) stochastic control, model predictive control, stochastic search, and robust/risk averse optimization, as well as related fields such as reinforcement learning and approximate dynamic programming. Also covered are both offline and online learning problems. Considerable emphasis is placed on modeling and computation.
  • ORF 548 Large-scale OptimizationSurvey of methods for solving large-scale optimization problems, with an emphasis on implementation issues. Topics are chosen from the following: linear programming-basis partitioning methods, Dantzig-Wolfe decomposition, Benders' decomposition, and interior point methods; nonlinear programming-conjugate gradient algorithms, quasi-Newton methods, sparse Newton methods, reduced gradient techniques, and trust-region strategies; and parallel optimization-distributed algorithms and single-machine algorithms.
  • ORF 550 Topics in Probability
  • ORF 551 Random Measures and Levy Processes 
  • ORF 553 Stochastic Differential EquationsThe general theory of martingales and semimartingales; stochastic integrals and stochastic differential equations; diffusion processes; Brownian flows, mass transport by flows.
  • ORF 554 Markov ProcessesMarkov processes with general state spaces; transition semigroups, generators, resolvants; hitting times, jumps, and Levy systems; additive functionals and random time changes; killing and creation of Markovian motions.
  • ORF 557 Stochastic Analysis SeminarRecent developments in the theory and applications of the analysis of random processes and random fields. Applications include financial engineering, transport by stochastic flows, and statistical imaging.
  • ORF 558 Stochastic Analysis SeminarRecent developments in the theory and applications of the analysis of random processes and random fields. Applications include financial engineering, transport by stochastic flows, and statistical imaging.
  • ORF 562 Transportation and Logistics PlanningOperations research in transportation, logistics, and operations planning; static, dynamic, and stochastic inventory models; multilocational inventory methods and their extention to dynamic fleet management; dynamic routing over transportation networks; equilibrium models for traffic assignment; and the vehicle routing problem. The focus of the course is the modeling process, and the formulation and solution of mathematical problems that arise in an operational context. Additional techniques are introduced as needed. The course is open to advanced undergraduates. Prerequisites: optimization and stochastic models.
  • ORF 566 High Dimensional StatisticsCourse is on statistical theory and methods for high-dimensional statistical learning and inferences arising from processing massive data from various scientific disciplines. Emphasis is given to penalized likelihood methods, independence screening, large covariance modeling, and large-scale hypothesis testing. The important theoretical results are proved.
  • ORF 569 Special Topics in Statistics and Operations ResearchAdvanced topics in statistics and operations research or the investigation of problems of current interest.
  • ORF 570 Special Topics in Statistics and Operations ResearchAdvanced topics in statistics and operations research or the investigation of problems of current interest.
  • ORF 574 Special Topics in Investment Science
  • ORF 575 Financial Engineering Seminar
  • Statement of Academic Purpose
  • Application Fee: $90
  • Resume/Curriculum Vitae
  • Recommendation Letters
  • Transcripts
  • Fall Semester Grades
  • All applicants are required to select a subplan when applying.
  • All applicants are required to submit a GRE general test. A mathematics subject test is strongly recommended.
  • M.S.E. applicants are required to have the endorsement of a faculty member who is willing to supervise them prior to submitting an application.
  • M.S.E. applicants are required to submit a Statement of Financial Resources.
  • Assistantships
  • Global Education
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